The Impact of AI on Modern Clinical Trials

Everywhere I look, AI is being hailed as the next big thing in clinical research. In my opinion, it’s not hype as it’s already reshaping how trials are designed, managed, and analysed.

For those within Pharma and Life Sciences, this shift is both exciting and unsettling. We’re watching technology promise to do in hours what once took months.

That said, while the potential is clear, adoption is still in the early stages. A handful of organisations are experimenting and seeing promising results, but for most, AI remains a cautious “test and learn” journey rather than business as usual.

The Upside: Smarter, Faster, More Predictive Trials

Let’s start with the good news. AI is genuinely helping clinical teams work smarter and more efficiently, and its impact is becoming more visible across four key areas.

1. Smarter Site Selection

Selecting the right sites has traditionally relied on a mix of data and intuition. Today, AI can analyse historical site performance, patient demographics, and investigator experience to identify sites most likely to deliver strong enrolment and high-quality data. This not only improves efficiency but also reduces operational risk and strengthens planning.

2. Faster Patient Recruitment

Patient recruitment has long been one of the most challenging aspects of clinical research. AI can accelerate this process by matching patients to protocols with far greater precision, using medical records, demographic information, and even social determinants of health. According to PharmaVoice, these tools are already helping teams shorten timelines, sometimes by weeks or even months creating a big operational advantage.

3. Predictive, Real-Time Data Insights

AI is also transforming how trials are monitored. Machine learning models can flag anomalies in near real time, helping teams prioritise issues and protect data quality. A recent example is the European Medicines Agency’s qualification of the AIM-NASH tool for liver disease trials a significant signal that regulators are willing to recognise validated AI models as reliable components of clinical research. From my perspective, it’s a big turning point.

4. Smarter Retention and Archiving

AI is redefining how organisations manage retention and archiving. Instead of merely storing documents, AI enables tracking of model versions, training datasets, and metadata that explain how decisions were reached. It can automatically organise unstructured or messy data, making it far easier to search through years of material. With semantic search, users no longer need to remember file names, they can simply ask a question in plain language and find the relevant information. In short, archives evolve from static storage into dynamic, living knowledge assets that grow more valuable over time.

The Reality Check: Oversight Still Matters

Working for Arkivum, I see every day how vital it is to manage and protect clinical data over the long term. In my opinion, the biggest challenge with AI in clinical trials isn’t whether we should use it, it’s how to use it responsibly.

In October 2024, Arkivum hosted a panel discussion on ICH E6 (R3), where the introduction of AI sparked a lively debate. We ran a live poll during the session, and the results spoke volumes:

• 1% were using AI in multiple areas of their trials
• 17% were trialing AI in certain areas
• 82% were not using AI at all

To me, that clearly shows the industry’s caution, not due to lack of interest, but because governance, validation, and trust have not yet caught up with innovation.

Regulators are already asking the right questions:
How was the model trained?
Can its results be reproduced?
What’s the chain of custody for the training data?

Important Regulatory Developments

Regulation around AI is starting to evolve, and several key frameworks are shaping expectations:

• EMA’s Reflection Paper on AI emphasises transparency in model development, training data, and performance evaluation.
• FDA guidance highlights the importance of understanding model behaviour, managing training data, and ensuring consistent results throughout the software lifecycle.
• The GAMP® AI Guide provides hands-on advice for validating and governing AI in real operational environments.
• The EU AI Act takes a risk-based approach, with most clinical research tools categorised as high risk, requiring robust documentation, transparency, and oversight.

The key message here, if you’re using AI, you must be able to explain it, evidence it, and stand behind the decisions it supports.

The Future: Building Confidence in AI-Enabled Trials

In my view, the next phase of progress will depend on something simple: trust.

For those involved in clinical trials, whether you’re a Principal Investigator, QA Lead or Clinical Operations Manager success will come from embedding AI in ways that improve transparency, auditability, and ethical accountability. As powerful as AI becomes, it’s still our responsibility to ensure every insight can be traced, verified, and defended.

Final Thought

AI is transforming clinical trials, that’s undeniable. But the organisations that truly succeed won’t be the ones that adopt it the fastest; they’ll be the ones that adopt it with confidence, clarity, and care.

In my view, the future of clinical research isn’t AI versus humans.
It’s humans using AI responsibly, transparently, and with an unwavering commitment to data integrity.